小奇奇搬走了......

床是空的，桌子的另一边是空的，抽屉是空的，书架的上层是空的，衣柜的另一边是空的，浴室里储藏柜的另一边也是空的

拖鞋多了几双，不过没有小奇奇的蓝色“大拖”，乱七八糟的东西多了好多，都是小奇奇留下的......

卧谈没有了，八卦没有了，无辜的大眼睛不见了......

我也想搬家......

很不喜欢送别人离开的感觉......

 

祝福长得象泰国人的pseudo混血美女小奇奇：一路平安，身体健康，开心快乐，学习愉快......

开始毕设已经将近两个月了，虽说一直在按照superviser的要求看paper，了解背景资料，但是效率确是极低的。多亏了老板人很nice，每次都和颜悦色的帮我解决问题，指导方向。拖了快一个月的introduction今天说什么也要写完了，下周开始要努力工作喽！加油加油！

Introduction of Support Vector Machine (SVM)

 

Generally speaking, SVM (Support Vector Machine) is an algorithm used for data classification and regression. It was developed by Vladimir Vapnik and his colleagues [1] in 1995, based on statistical learning theory. In Geometry perspective, data can be represented as a set of feature vectors in n-dimension space. According to the attributes of training vector sets, SVM splits them into different feature spaces and generates a hyperplane which is a decision function in fact, to predict new unclassified data into the classes they should belong to. Optimal classification requires not only the correct separation but also the maximization of the separation distance (margin), in order to satisfy the structural risk minimization (SRM) notion. SVM settles the problem from the simplest one, which is 2-dimension linear classification. However, for most complex data, it is hard to find the linear hyperplane in low-dimension therefore they are mapped into higher dimension, which will increase the computational complexity dramatically. Therefore, Kernel function is introduced, in order to reduce the computation problem. Hence, proper choice of kernel function helps getting the classification function in higher dimension classifier much easier.

 

[1] V.N. Vapnik, “The Nature of Statistical LearningTheory,” Springer-Verlag, NewYork, ISBN 0-387-94559-8, 1995.